Performance and emission optimization of diesel engine by single and multi-objective genetic algorithms
作者： Kemal Tutuncu , Novruz Allahverdi
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摘要: In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance emissions a diesel engine. Population space initial population both GAs obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NOx, power (P), torque (Tq) air-flow rate (Afr) reduced to %7.7, %8.51, %30, %4 %7.4 respectively whereas HC increased at the %10.5 traditional objective GA. HC, CO2, P Sfc %17.6, %30.05, %31.8 %14.5 NOx %13 using GA with Nondominated Sorting Genetic Algorithm II (NSGA II). reduction against %31 have never been in previous studies. This shows effective usage MOGA NSGA engine parameters.
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